Network systems are utilized as communication links for everyday personal and business purposes. With the growth of network systems, particularly the Internet, and the advancement of computer hardware and software technology, network use ranges from simple communication exchanges such as electronic mail to more complex and data intensive communication sessions such as web browsing, electronic commerce, and numerous other electronic network services such as Internet voice, and Internet video-on-demand.
Network usage information does not include the actual information exchanged in a communications session between parties, but rather includes metadata (data about data) information about the communication sessions and consists of numerous usage detail records (UDRs). The types of metadata included in each UDR will vary by the type of service and network involved, but will often contain detailed pertinent information about a particular event or communications session between parties such as the session start time and stop time, source or originator of the session, destination of the session, responsible party for accounting purposes, type of data transferred, amount of data transferred, quality of service delivered, etc. In telephony networks, the UDRs that make up the usage information are referred to as a call detail records or CDRs. In Internet networks, usage detail records do not yet have a standardized name, but in this application they will be referred to as Internet detail records or IDRs. Although the term IDR is specifically used throughout this application in an Internet example context, the term IDR is defined to represent a UDR of any network.
Network usage information is useful for many important business functions such as subscriber billing, marketing & customer care, and operations management. Network usage data mediation systems are utilized for collecting, correlating, and aggregating network usage information as it occurs and creating UDRs as output that can be consumed by computer business systems that support the above business functions. Examples of these computer business systems include billing systems, marketing and customer relationship management systems, customer churn analysis systems, and data mining systems.
Especially for Internet networks, several important technological changes are key drivers in creating increasing demand for timely and cost-effective analysis of Internet usage information or the underlying IDRs.
One technological change is the dramatically increasing Internet access bandwidth at moderate subscriber cost. Most consumers today have only limited access bandwidth to the Internet via an analog telephony modem, which has a practical data transfer rate upper limit of about 56 thousand bits per second. When a network service provider's subscribers are limited to these slow rates there is an effective upper bound to potential congestion and overloading of the service provider's network. However, the increasing wide scale deployments of broadband Internet access through digital cable modems, digital subscriber line, microwave, and satellite services are increasing the Internet access bandwidth by several orders of magnitude. As such, this higher access bandwidth significantly increases the potential for network congestion and bandwidth abuse by heavy users. With this much higher bandwidth available, the usage difference between a heavy user and light user can be quite large, which makes a fixed-price, all-you-can-use pricing plan difficult to sustain; if the service provider charges too much for the service, the light users will be subsidizing the heavy users; if the service provider charges too little, the heavy users will abuse the available network bandwidth, which will be costly for the service provider.
Another technological change is the rapid growth of applications and services that require high bandwidth. Examples include Internet telephony, video-on-demand, and complex multiplayer multimedia games. These types of services increase the duration of time that a user is connected to the network as well as requiring significantly more bandwidth to be supplied by the service provider.
Another technological change is the transition of the Internet from “best effort” to “mission critical”. As many businesses are moving to the Internet, they are increasingly relying on this medium for their daily success. This transitions the Internet from a casual, best-effort delivery service into the mainstream of commerce. Business managers will need to have quality of service guarantees from their service provider and will be willing to pay for these higher quality services.
Due to the above driving forces, Internet service providers are moving from current, fixed-rate, all-you-can-use Internet access billing plans to more complex billing plans that charge by metrics, such as volume of data transferred, bandwidth utilized, service used, time-of-day, and subscriber class, which defines a similar group of subscribers by their usage profile, organizational affiliation, or other attributes.
An example of such a rate structure might include a fixed monthly rate portion, a usage allocation to be included as part of the fixed monthly rate (a threshold), plus a variable rate portion for usage beyond the allocation (or threshold). For a given service provider there will be many such rate structures for the many possible combinations of services and subscriber classes.
Network usage analysis systems provide information about how the service provider's services are being used and by whom. This is vital business information that a service provider must have in order to identify fast moving trends, establish competitive prices, and define new services or subscriber classes as needed. Due to the rapid pace that new Internet services are appearing, the service provider must have quick access to this vital information. Known analysis packages feed the network usage data into large databases, and then perform subsequent analysis on the data at a later time. These database systems can get quite large. A service provider with one million subscribers can generate tens of gigabytes of usage data every day. Although the technology for storing vast amounts of data has been steadily improving, Internet traffic is growing at a much faster pace. Storing and managing all of this data is expensive and may eventually become prohibitive. Large and expensive supporting hardware is required (e.g., terabyte disk storage, back-up systems) and expensive relational database management software systems (RDBMS) are required to support very high transaction rates and large data sets. Further, database administrative personnel must be employed to support and maintain these large database management systems.
Once the type of analysis is determined, data mining and analysis software systems are utilized to query and analyze the large amounts of network usage information stored in the databases. The use of data mining and analysis software systems often requires additional business analysis consulting services, additional support hardware, and data mining software licenses. Further, given the amount of data that needs to be processed, the total latency of time aging of the data can be quite long. It may take days to weeks to extract the needed information. For example, if one were to use a standard SQL reporting software system to analyze the effect of changing to a different threshold in a rate structure including a fixed monthly rate portion plus a variable rate portion, it may take several days to produce the result.
One type of analysis disclosed in U.S. patent application Ser. No. 09/548,124, filed Apr. 12, 2000, entitled “Internet Usage Analysis System and Method,” utilizes statistical models for analyzing network usage data. Since the raw network usage data is too voluminous to search quickly, statistical models are constructed that are representative of the raw network usage data. These statistical models are stored, and may be subsequently analyzed for solving network usage problems.
For reasons stated above and for other reasons presented in greater detail in the Description of the Preferred Embodiment section of the present specification, more advanced techniques are required in order to use the statistical models for determining revenue associated with excess usage above a threshold for real time (or near real time) financial modeling.